一种改进的基于局部密度的聚类算法

doi:10.11959/j.issn.1000-0801.2016008

Abstract

Abstract:

Clustering analysis is an important and challenging research field in machine learning and data mining.A fast and effective clustering algorithm based on the idea of local density was proposed by Alex.But the number of clusters and cluster centers in the algorithm were determined by hand.Therefore,the candidates of cluster centers based on local density were firstly selected and then density connectivity method was used to optimize the candidates.The classes of samples are the same as the nearest center with bigger local density.Experiments show that the proposed method has a better cluster efficiency and can handle the problems of uncertain cluster number and cluster centers.

Key words: local density, cluster center, evaluation criterion

Xiaohui GUAN,Yaguan QIAN,Xinxin SUN. An improved clustering algorithm based on local density[J]. Telecommunications Science, 2016, 32(1): 54-59.

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References 22

[1]	周涛，陆惠玲 . 数据挖掘中聚类算法研究进展[J]. 计算机工程与应用， 2012,48（12）:100-109. ZHOU T , LU H L . Clustering algorithm research advances on data mining[J]. Computer Engineering and Applications, 2012,48（12）:100-109.
[2]	ZHANG T , RAMAKRISHNAN R , LIVNY M . BIRCH:an efficient data clustering method for very large databases[C]// Proceedings of 1996 ACM-SIGMOD International Conference Management of Data, June 4-6, 1996, Montreal,Quebec,Canada. New York: ACM Press, 1996: 103-114.
[3]	GUHA S , RASTOGI R , SHIM K . CURE:an efficient clustering algorithm for large database[J]. Information Systems, 2001,26（1）:35-58.
[4]	MACQUEEN J . Some methods for classification and analysis of multivariate observations[C]// Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, June 21-18, 1965, Berkeley,California,USA. California: University of California Press, 1967: 281-297.
[5]	KAUFMAN L , ROUSSEEUW P J . Finding Groups in Data:An Introduction to Cluster Analysis[M]. New York:John Wiley &Sons, 1990.
[6]	HUANG Z . Extensions to the k-means algorithm for clustering large data sets with categorical values[J]. Data Mining and Knowledge,Discovery II, 1998（2）:283-304.
[7]	倪巍伟，陈耿，吴英杰 . 一种基于局部密度的分布式聚类挖掘算法[J]. 软件学报， 2008,19（9）:2339-2348. NI W W , CHEN G , WU Y J . Local density based distributed clustering algorithm[J]. Journal of Software, 2008,19（9）:2339-2348.
[8]	ANKERST M , BREUNIG M , KRIEGEL H P , et al. OPTICS:ordering points to identify the clustering structure[C]// Proceedings of 1999 ACM-SIGMOD International Conference Management of Data（SIGMOD'99）, June 1-3, 1999, Philadelphia,Pennsylvania,USA. New York: ACM Press, 1999: 49-60.
[9]	HINNEBURG A , KEIM D A . An efficient approach to clustering in large multimedia databases with noise[C]// Proceedings of 1998 International Conference Knowledge Discovery and Data Mining, August 27-31, 1998, New York,USA. New York: ACM Press, 1998: 58-65.
[10]	WANG W , YANG J , MUNTZ R . STING:a statistical information grid approach to spatial data mining[C]// Proceedings of 1997 International Conference Very Large Data Bases, August 2-29, 1997, Athens,Greece. New York: ACM Press, 1997: 186-195.
[11]	AGRAWAL R , GEHRKE J , GUNOPULOS D , et al. Automatic subspace clustering of high dimensional data for data mining applications[C]// Proceedings of 1998 ACM-SIGMOD International Conference Management of Data, June 2-4, 1998, Seattle,Washington,USA. New York: ACM Press, 1998: 94-105.
[12]	FISHER D . Improving inherence through conceptual clustering[C]// Proceedings of 1987 AAAI Conference, July 13-17, 1987, Seattle,Washington,USA. [S.l.]: AAAI Press, 1987: 461-465.
[13]	FAYYAD V M , PIATETSKY S G , SMYTH P et al. Bayesian Classification （AutoClass）:Theory and Result.Advances in Knowledge Discovery and Data Mining[M]. Bridge City: The MIT Press, 1996:153-180.
[14]	TEUVO K . The self-organizing map[J]. Neurocomputing, 1998,21（13）:1-6.
[15]	CAI W L , CHEN S C , ZHANG D Q . Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation[J]. Pattern Recognition, 2007,40（3）:825-833.
[16]	BASU B , SRINIVAS V V , . Regional flood frequency analysis using kernel-based fuzzy clustering approach[J]. Water Resources Research, 2014,50（4）:3295-3316.
[17]	LI X , WONG H S , WU S . A fuzzy minimax clustering model and its applications[J]. Information Sciences, 2012,186:114-125.
[18]	周林，平西建，徐森，等. 基于谱聚类的聚类集成算法[J]. 自动化学报， 2012,38（8）:1335-1342. ZHOU L , PING X J , XU S , et al. Cluster ensemble based on spectral clustering[J]. Acta Automatica Sinica, 2012,38（8）:1335-1342.
[19]	陈黎飞，郭躬德，姜青山 . 自适应的软子空间聚类算法[J]. 软件学报， 2010（10）:2513-2523. CHEN L F , HUO G D , JIANG Q S . Adaptive algorithm for soft subspace clustering[J]. Journal of Software, 2010（10）:2513-2523.
[20]	SUN H , WANG S , JIANG Q . FCM-based model selection algorithms for determining the number of cluster[J]. Pattern Recognition, 2004,37（10）:2027-2037.
[21]	FREY B J , DUECK D , . Clustering by passing messages between data points[J]. Science, 2007,315（5814）:972-976.
[22]	ALEX R , ALESSANDRO L , . Clustering by fast search and find of density peaks[J]. Science, 2014,344（6191）:1492-1496.

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数据集	评价指标	本文算法	K-means	DBSCAN
Iris数据集	purity	0.906 7	0.881 7	0.666 7
	R指数	0.892 3	0.839 4	0.776 3
	F-measure	0.904 8	0.885 3	0.655 6
Wine数据集	purity	0.893 3	0.724 9	0.696 6
	R指数	0.865 4	0.691 9	0.721 2
	F-measure	0.895 8	0.704 3	0.706 0
leuk72_3k数据集	purity	0.958 3	0.958 3	0.916 7
	R指数	0.947 2	0.947 2	0.896 7
	F-measure	0.956 8	0.956 8	0.915 4
Glass数据集	purity	0.752 2	0.710 1	0.579 7
	R指数	0.762 4	0.683 3	0.638 5
	F-measure	0.717 1	0.726 8	0.516 2
4k2_far数据集	purity	1	0.807 5	0.987 5
	R指数	1	0.930 5	0.985 9
	F-measure	1	0.903	0.986 8

An improved clustering algorithm based on local density

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References 22

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